Hon Hai Research Institute Launches Traditional Chinese LLM With Reasoning Capabilities

Rhea-AI Summary

Hon Hai Research Institute has launched FoxBrain, the first Traditional Chinese Large Language Model (LLM), developed in just four weeks using 120 NVIDIA H100 GPUs. Based on Meta Llama 3.1 architecture with 70B parameters, FoxBrain demonstrates superior performance in mathematics and logical reasoning compared to existing models.

The model, backed by Foxconn (TWSE:2317), will be open-sourced and was initially designed for internal applications including data analysis, decision support, and code generation. FoxBrain utilized efficient training methods and resource optimization, processing 98B tokens of high-quality pre-training data across 24 topic categories.

The model's development showcases Taiwan's AI capabilities, achieving results comparable to world-class AI models despite computational resources. FoxBrain will be integrated into Foxconn's three major platforms: Smart Manufacturing, Smart EV, and Smart City, with its results to be presented at NVIDIA GTC 2025 on March 20.

Positive

• Development of proprietary AI technology demonstrating competitive capabilities
• Efficient resource utilization: 4-week development vs industry standards
• Superior performance metrics in mathematics and reasoning compared to existing models
• Strategic application across three major business platforms

Negative

• Still trails DeepSeek's distillation model in mathematical reasoning performance

First version by AI Research Center performs well in mathematics and reasoning

TAIPEI, March 10, 2025 /PRNewswire/ -- Hon Hai Research Institute announced today the launch of the first Traditional Chinese Large Language Model (LLM), setting another milestone in the development of Taiwan's AI technology with a more efficient and lower-cost model training method completed in just four weeks.

The institute, which is backed by Hon Hai Technology Group ("Foxconn") (TWSE:2317), the world's largest electronics manufacturer and leading technological solutions provider, said the LLM – code named FoxBrain – will be open sourced and shared publicly in the future. It was originally designed for applications used in the Group's internal systems, covering functions such as data analysis, decision support, document collaboration, mathematics, reasoning and problem solving, and code generation.

FoxBrain not only demonstrates powerful comprehension and reasoning capabilities but is also optimized for Taiwanese users' language style, showing excellent performance in mathematical and logical reasoning tests.

"In recent months, the deepening of reasoning capabilities and the efficient use of GPUs have gradually become the mainstream development in the field of AI. Our FoxBrain model adopted a very efficient training strategy, focusing on optimizing the training process rather than blindly accumulating computing power," said Dr. Yung-Hui Li, Director of the Artificial Intelligence Research Center at Hon Hai Research Institute. "Through carefully designed training methods and resource optimization, we have successfully built a local AI model with powerful reasoning capabilities."

The FoxBrain training process was powered by 120 NVIDIA H100 GPUs, scaled with NVIDIA Quantum-2 InfiniBand networking, and finished in just about four weeks. Compared with inference models recently launched in the market, the more efficient and lower-cost model training method sets a new milestone for the development of Taiwan's AI technology.

FoxBrain is based on the Meta Llama 3.1 architecture with 70B parameters. In most categories among TMMLU+ test dataset, it outperforms Llama-3-Taiwan-70B of the same scale, particularly exceling in mathematics and logical reasoning (For TMMLU+ benchmark of FoxBrain, please refer to Fig.1). The following are the technical specifications and training strategies for FoxBrain:

• Established data augmentation methods and quality assessment for 24 topic categories through proprietary technology, generating 98B tokens of high-quality pre-training data for Traditional Chinese
• Context window length: 128 K tokens
• Utilized 120 NVIDIA H100 GPUs for training, with total computational cost of 2,688 GPU days
• Employed multi-node parallel training architecture to ensure high performance and stability
• Used a unique Adaptive Reasoning Reflection technique to train the model in autonomous reasoning

In test results, FoxBrain showed comprehensive improvements in mathematics compared to the base Meta Llama 3.1 model. It achieved significant progress in mathematical tests compared to Taiwan Llama, currently the best Traditional Chinese large model, and surpassed Meta's current models of the same class in mathematical reasoning ability. While there is still a slight gap with DeepSeek's distillation model, its performance is already very close to world-leading standards.

FoxBrain's development – from data collection, cleaning and augmentation, to Continual Pre-Training, Supervised Finetuning, RLAIF, and Adaptive Reasoning Reflection – was accomplished step by step through independent research, ultimately achieving benefits approaching world-class AI models despite limited computational resources. This large language model research demonstrates that Taiwan's technology talent can compete with international counterparts in the AI model field.

Although FoxBrain was originally designed for internal group applications, in the future, the Group will continue to collaborate with technology partners to expand FoxBrain's applications, share its open-source information, and promote AI in manufacturing, supply chain management, and intelligent decision-making.

During model training, NVIDIA provided support through the Taipei-1 Supercomputer and technical consultation, enabling Hon Hai Research Institute to successfully complete the model pre-training with NVIDIA NeMo. FoxBrain will also become an important engine to drive the upgrade of Foxconn's three major platforms: Smart Manufacturing. Smart EV. Smart City.

The results of FoxBrain is scheduled to be shared for the first time at a major conference during NVIDIA GTC 2025 Session Talk "From Open Source to Frontier AI: Build, Customize, and Extend Foundation Models" on March 20.

About Hon Hai Research Institute

The institute has five research centers. Each center has an average of 40 high technology R&D professionals, all of whom are focused on the research and development of new technologies, the strengthening of Foxconn's technology and product innovation pipeline, efforts to support the Group's transformation from "brawn" to "brains", and the enhancement of the competitiveness of Foxconn's "3+3" strategy.

About Foxconn here.

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SOURCE Hon Hai Research Institute

FAQ

What are the key technical specifications of FoxBrain, Hon Hai's new LLM (HNHPF)?

FoxBrain uses Meta Llama 3.1 architecture with 70B parameters, 128K token context window, trained on 120 NVIDIA H100 GPUs over 2,688 GPU days with 98B tokens of pre-training data.

How does FoxBrain's performance compare to other Traditional Chinese language models?

FoxBrain outperforms Llama-3-Taiwan-70B in most TMMLU+ categories, particularly in mathematics and logical reasoning, approaching world-leading standards.

When will Hon Hai (HNHPF) make FoxBrain available as open-source?

While Hon Hai has announced FoxBrain will be open-sourced in the future, no specific release date has been provided.

What business applications will FoxBrain serve for Hon Hai (HNHPF)?

FoxBrain will power Foxconn's Smart Manufacturing, Smart EV, and Smart City platforms, supporting data analysis, decision support, document collaboration, and code generation.